Gen AI Engineer

Core Responsibilities

• Design, develop, and deploy GenAI and ML solutions using Azure OpenAI, Azure ML, Azure AI Studio, and related services
• Build LLMpowered applications using: Prompt engineering, Tool/function calling, RetrievalAugmented Generation (RAG) and Agentic AI design patterns
• Develop AgenttoAgent (A2A) workflows for task decomposition, orchestration, and collaboration
• Implement Model Context Protocol (MCP) to standardize tool access, memory, and context management across agents

• Apply MLOps and LLMOps practices:

• CI/CD for models and prompts and Versioning, monitoring, evaluation

• Failure detection and mitigation (hallucinations, drift, prompt regressions)

• Implement LLM security and guardrails:

• Prompt injection prevention,

• Data leakage protection and Output moderation and policy enforcement

• Finetune and adapt models for economy - token economics, latency, and cost optimization
• Build and expose AI capabilities via secure APIs and microservices
• Collaborate with architects, data engineers, and platform teams
• Actively research and document emerging GenAI techniques and tools
• Mentor junior engineers and contribute to internal knowledge assets

Technology Stack

• Programming: Python (PEP8 / Google Python standards)
• LLMs: Azure OpenAI, OpenAI, opensource and local LLMs
• AI Platforms: Azure ML, Azure Databricks, Azure AI Services
• Agent Frameworks: LangChain, LangGraph, Semantic Kernel (or equivalent)
• Protocols & Patterns: MCP, A2A, RAG variants
• DevOps: Docker, REST APIs, CI/CD pipelines
• Datastores: SQLbased RDBMS, Vector databases
• Graph databases: Neo4j, GraphDB

Soft Skills

• Strong ownership mindset and delivery focus
• Ability to work in agile, fastchanging environments
• Effective collaboration across engineering and architecture teams